Ch1: Introduction Prepared by: Hanan Hardan Image Processing Ch1: Introduction Prepared by: Hanan Hardan

“One picture is worth more than ten thousand words” Introduction “One picture is worth more than ten thousand words”

References “Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002 Much of the material that follows is taken from this book “Machine Vision: Automated Visual Inspection and Robot Vision”, David Vernon, Prentice Hall, 1991 Available online at: homepages.inf.ed.ac.uk/rbf/BOOKS/VERNON/

Contents This lecture will cover: What is a digital image? What is digital image processing? History of digital image processing State of the art examples of digital image processing Key stages in digital image processing

What is a Digital Image? A digital image: is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels Real world is continuous – an image is simply a digital approximation of this.

What is a Digital Image? (cont…) Pixels: Elements of the digital image , each has intensity. Intensity of pixel: the amplitudeغزارة of gray level (in gray scale images) 1 pixel

digital image processing What is digital Image? An image can be defined as function of 2 variables , f(x,y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x , y) is called the intensity of the image at that point Digital image is composed of a finite number of elements, each one has a particular location and value.. These element are called picture elements, image elements or pixels. Note: images can be: binary, grayscale, color.

What is digital Image?

digital image processing The image consists of finite number of pixels ( f(x,y) ) What is digital image? Every pixel Is an intersection تقاطع between a row and a column. every pixel has intensity كثافة Ex: f(4,3)= 123 Refers to a pixel existing on the intersection between row 4 with column 3, and its intensity is 123. Remember digitization implies that a digital image is an approximation of a real scene pixel

digital image processing Binary Images Remember: images can be: binary, grayscale, color. Binary images are images that have been quantized to two values, usually denoted 0 and 1, but often with pixel values 0 and 255, representing black and white.

Binary Images

Grayscale Images A grayscale (or graylevel) image is simply one in which the only colors are shades of gray (0 – 255)

Grayscale Images

Color Images Color image: A color image contains pixels each of which holds three intensity values corresponding to the red, green, and blue or( RGB)

Color Images

What is digital image processing? Digital image processing focuses on two major tasks Improve image quality(pictorial information) for human perception and interpretation Processing of image data for storage, transmission and representation for autonomous machine perception

Computer Graphics: the creation of image Image processing fields: Computer Graphics: the creation of image Image processing: enhancement or other manipulation of the image Computer vision: analysis of the content

What are digital image processing levels? low level processes: Input and output are images Tasks: Primitive operations, such as, image processing to reduce noise, contrast enhancement and image sharpening مثال صورة قديمة نريد تحسينها

What are digital image processing levels? Mid-Level Processes: Inputs, generally, are images. Outputs are attributes extracted from those images (edges, contours, identity of individual objects) Tasks: Segmentation (partitioning an image into regions or objects) Description of those objects to reduce them to a form suitable for computer processing Classifications (recognition) of objects مثال: صورة لكرسي نريد تعديلها حاسوبيا لنبرز حوافه

What are digital image processing levels? High-Level Processes Input: Attributes Output: Understanding Tasks: recognizing objects Image analysis and computer vision(Analysis of the image content) Examples: Scene understanding مثال: صورة لمشتبه فيه نريد الحاسوب ان يتعرف عليه

Uses of DIP Image enhancement/restoration Artistic effects Medical visualisation Law enforcement Human computer interfaces

Examples: Image Enhancement One of the most common uses of DIP techniques: improve quality, remove noise etc

Examples: The Hubble Telescope Launched in 1990 the Hubble telescope can take images of very distant objects However, an incorrect mirror made many of Hubble’s images useless Image processing techniques were used to fix this

Examples: Artistic Effects Artistic effects are used to make images more visually appealing, to add special effects and to make composite images

Examples: Medicine Take slice from MRI (Magnetic Resounance Imaging) scan of a heart, and find boundaries between types of tissue Image with gray levels representing tissue density Use a suitable filter to highlight edges

Examples: GIS Geographic Information Systems Digital image processing techniques are used extensively to manipulate satellite imagery Terrain classification (التضاريس) Meteorology (الأرصاد الجوية)

Examples: Law Enforcement Image processing techniques are used extensively by law enforcers Number plate recognition for speed cameras Fingerprint recognition

Examples: HCI Try to make human computer interfaces more natural Face recognition

Fundamental steps in digital image processing

1.Image Acquisition: (capturing an image in digital form) Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 1: Image Acquisition The image is captured by a sensor (eg. Camera), and digitized if the output of the camera or sensor is not already in digital form, using analogue-to-digital convertor

2.Image Enhancement: making an image look better in a subjective way. Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 2: Image Enhancement The process of manipulating an image so that the result is more suitable than the original for specific applications. Enhancement techniques are so varied, and use so many different image processing approaches The idea behind enhancement techniques is to bring out details that are hidden, or simple to highlight certain features of interest in an image.

3.Image Restoration: improving the appearance of any image objectively. Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 3: Image Restoration - Improving the appearance of an image (محاولة اعادة الصوره الى طبيعتها) - Tend to be based on mathematical or probabilistic models of image degradation.

4.Morphological Processing: extracting image components that are useful in the representation and description of shape Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step4 : Morphological Processing Tools for extracting image components that are useful in the representation and description of shape.

5.Segmentation: partitioning an image into its constituent parts or objects. Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 5: Image Segmentation Segmentation procedures partition an image into its parts or objects. Computer tries to separate objects from the image background. الهدف: الحصول على الاجزاء المهمه او لاعادة تشكيل الصورة لتعطي معنى مختلف

6.Object Recognition: assigning a label to an object based on its descriptors Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step6 : Recognition and Interpretation Recognition: the process that assigns label to an object based on the information provided by its description. تميز محتوى الصوره

7. Representation & Description: boundary representation vs 7.Representation & Description: boundary representation vs. region representation. Boundary descriptors vs. region descriptors Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 7: Representation and Description Make a decision whether the data should be represented as a boundary or as a complete region: Boundary representation: focus on external shape characteristics, such as corners and inflections. Region representation: focus on internal properties, such as texture or skeleton shape تمثيل الصوره ووصفها

8.Image Compression: reducing the stored and transmitted image data. Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 8: Compression Techniques for reducing the storage required to save an image or the bandwidth required to transmit it.

9.Colour Image Processing: color models and basic color processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Fundamental Steps in DIP Step 9: Colour Image Processing Use the colour of the image to extract features of interest in an image